For most of my career I never really had to look for work, interviews came quickly and the jobs followed. That stopped in 2026, in an industry shedding designers to the same tools I use every day. So I did what I do with anything uncertain, and started measuring it.
Since April: 70 applications, 5 human conversations, 2 requests to see the work, 0 offers. Companies are anonymised, every number is real. I enjoyed building this, and I keep it public because the numbers should be seen. It is a snapshot of a search that is still running.
01 · Where applications die
37 applications reached a CV screen. 5 became a conversation with a human. The screen is where this search dies, before anyone has seen the work.
02 · How fast they die
A same-day rejection is a filter, not a decision. Filled dots had a human involved at some point; hollow ones never did. 12 applications were never answered at all.
A human was involvedNever any humanStill open
* month-precision records: the log dates most early entries by month only.
03 · Does my own fit assessment predict anything?
Almost flat. A 5/5 fit (#9) and a 4.5/5 (#40) died at the CV screen like the 2/5s. The two applications that got as far as the work were a 3.5 (#33) and a 4 (#44). Whatever the screen measures, it is not fit.
Closed (rejected or ghosted)Still open
* the fit score is my own subjective assessment, scored when I applied. The method note at the foot of the page says how it is built.
04 · Channel against furthest stage
60 cold applications produced 3 conversations, including the process that has gone furthest: a take-home exercise (#44). Until then, the furthest process had begun with the company reaching out in September, five months after it had rejected the same profile’s cold application in April. The samples are small, so read this as a hint.
05 · Self-audit: blockers I knew about and applied anyway
40 of 70 applications carried at least one blocker I knew about at submission. This chart judges my own choices, and it is the only one here that could change behaviour.
06 · Did better materials change anything?
The CV was rebuilt three times and a new portfolio shipped, and the line rose through September. Split it by channel and the rise comes from applications that did not go in cold. Since August, 6 of 9 applications that went through an intermediary, an inbound recruiter or a direct email reached a human. In the same months, 1 of 29 cold applications did.
07 · Geography and outcome
Solid is rejected, muted is ghosted, teal is still open. No geography behaves meaningfully better: the screen is the screen everywhere.
Rejected · 34Ghosted · 12Still open · 24
08 · Ghosting over time
12 of 70 applications, about 17%, were never answered. Recent months read low only because ghosting takes two months to earn its name.
Share never answeredToo recent to count
* too recent to have earned the label.
09 · Domain sprawl
70 applications went to 33 different domains, and 14 of those domains got exactly one. Domain mismatch was one of the two most common known blockers, and of the 2 rejections that came with specific feedback, one named domain fit. The sprawl probably explains part of the rejections. This chart argues for concentration.
1 application each
AI talent · Community SaaS · Construction · Consumer health · Design tools · Employee benefits · Energy · Gambling · Home services · Instruments · Marketplaces · Mobility · Public sector · Research tools
Read the other way, the postings are a rough map of who was hiring senior designers in 2026. Across all 93 I assessed, including the 23 I passed on, the top three domains were healthtech, fintech and consulting. My own searches and job alerts chose what I saw, so it describes one search.
All 93 postings assessed, sent or not Healthtech 13 · Fintech 10 · Consulting 7 · Consumer apps 4 · Cybersecurity 4 · Developer tools 4 · SaaS 4 · Compliance 3
10 · Did I get pickier?
Yes. From June, the average fit score of what I applied to climbed every month. The selection tightened; the response to cold applications did not follow.
* same fit score as chart 03: mine, subjective, scored at submission.
11 · A named person
Among cold applications, 5 of the 14 with a named person reached a human, and 3 got as far as a conversation. Without a name, 1 of 46 reached a human, and none got as far as a conversation.
A conversationA human replied, no conversationNo human
* the log keeps a name when I had one, from the posting or my own outreach, but sometimes only once someone replied, so part of this gap is the reply itself. Small samples.
12 · The waiting room
24 applications are open. The oldest, #35, has waited 46 days, and none is within ten days of the line where silence starts to count as ghosting.
13 · What a no looks like
Of 34 rejections, 25 were template emails and 3 came automatically the same day. 2 said anything specific about the application.
14 · Did they say what it pays?
33 of the 93 postings I assessed stated a salary. By region, the highest share was United Kingdom (5 of 9) and the lowest Nordics & Baltics (1 of 9).
Salary statedNot stated
34rejections
2came with evidence a human assessed anything
2processes asked to see the work
Method
Every application is logged with dates, stage reached, channel, my own fit score, and blockers known at submission. Companies and people are removed at extraction, before this page is built, and a test fails the build if a name reaches it. 23 roles assessed but never applied to are excluded from every rate.
The fit score is subjective by design, my own reading of how well I match the role. It is built the same way every time. Before sending, each application gets a written assessment, strengths, gaps, and why I applied, and that assessment is condensed into a score from 1, poor, to 5, exceptional, in half steps. It is scored with what I knew at submission, and the outcome does not revise it afterwards.